The present application relates generally to patient monitoring systems, and more specifically to a patient activity monitoring system providing a simplified user interface, customizable alarms, and automatic customized report generation. The presently disclosed patient activity monitoring system enables caregivers to work more efficiently and with reduced cost, while increasing the quality and level of care provided to patients.
Patient monitoring systems are known that employ advanced sensor, electronics, and communications technologies for remotely monitoring one or more characteristics of a patient. For example, a conventional patient monitoring system may comprise a remote monitoring unit associated with a patient, a central monitoring unit, and a communications device for establishing a communications link between the remote monitoring unit and the central monitoring unit. The remote monitoring unit typically employs a sensor for generating data representative of, e.g., a measured physiological characteristic of the patient such as the patient's heartbeat. Further, the communications device typically establishes a communications link for transmitting the patient data from the remote unit to the central unit either periodically or when a predetermined warning limit is exceeded. Upon receipt of the patient data, the central monitoring unit may then provide a visible and/or audible indication to a caregiver at the centralized location indicating whether or not the patient requires immediate attention.
Although patient monitoring systems like the conventional system described above have generally enhanced the quality of care given to patients, such patient monitoring systems have drawbacks when employed in settings such as hospital wards and nursing homes. In such settings, the patients requiring intermittent or continuous monitoring may significantly outnumber the caregivers available at the hospital or nursing home location. Further, each patient may have more than one condition that needs to be monitored. Still another drawback of conventional systems is that they often generate false alarms because they typically do not adequately monitor large sensing areas, i.e., conventional systems typically fail to provide large aperture sensing. As a result, patients may wander off the sensed area, thereby causing false alarms to be generated. Possible solutions to these problems include increasing the number of patient monitoring systems and/or the number of caregivers at the hospital or nursing home site. However, both of these solutions can significantly increase the cost of providing quality health care to patients.
Not only may certain physiological characteristics such as the heartbeat of a patient require monitoring by caregivers, but certain activities of patients may also require significant caregiver supervision. For example, there may be problems associated with a patient getting out of bed without supervision or assistance. Such a patient may suffer a fall resulting in substantial physical injury. In the event a patient requires extended bed rest to recover from a fall, he or she may require assistance to turn over after being inactive in bed for an extended period of time. Moreover, such a patient may experience incontinence or may simply become agitated from the extended bed rest. In each case, the patient may require assistance from a caregiver whose attention may currently be directed toward another patient at the healthcare facility. Conventional patient monitoring systems like the system described above generally do not provide caregivers the information they need to operate efficiently and with a high degree of care in settings such as hospital wards and nursing homes, in which each caregiver may be responsible for multiple patients requiring various types and levels of assistance.
It would therefore be desirable to have a patient monitoring system that allows caregivers of multiple patients to operate more efficiently and with reduced cost. Such a system would allow data to be collected over time so that data mining can be used to observe trends in caregiver response, fall trends, etc. Caregivers may then use this data to generate intervention plans for specific patients in order to improve the quality and level of care, as required.